XGBoost wrapper for seurat project in R

source("tianfengRwrappers.R")
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If you use clusterProfiler in published research, please cite:
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CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

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  in R. Bioinformatics 2014.

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library(xgboost)

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library(SHAPforxgboost)
library(lambda.r)

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ds0 <- readRDS("ds0.rds")
ds1 <- readRDS("ds1.rds")
ds2 <- readRDS("ds2.rds")
XGBoost_predict_from_seuobj <- function(seuobj, bst_model, is_highvar = T, seed = 7, celltype_assign = 2)
{
  #return a updated seurat object with new metadata named confidence and projected_idents
  seuobj_label <- as.numeric(as.character(Idents(seuobj)))
  if(is.na(seuobj_label[1])) # check vaild Idents
  {
    stop("Please ensure that seurat idents are in numeric forms")
  }
  temp <- get_data_table(seuobj, highvar = T, type = "data")
  seuobj_data <- matrix(data = 0, nrow = bst_model$nfeatures, ncol = length(colnames(temp)), 
                     byrow = FALSE, dimnames = list(bst_model[["feature_names"]],colnames(temp)))
  intersect_features <- intersect(bst_model[["feature_names"]], rownames(temp))
  seuobj_data[intersect_features,] <- temp[intersect_features,]
  rm(temp)
  

  # colnames(seuobj_data) <- NULL
  seuobj_test_data <- list(data = t(as(seuobj_data,"dgCMatrix")), label = seuobj_label)
  seuobj_test <- xgb.DMatrix(data = seuobj_test_data$data,label = seuobj_test_data$label)
  
  #预测结果
  predict_seuobj_test <- predict(bst_model, newdata = seuobj_test)
  
  predict_prop_seuobj <<- matrix(data=predict_seuobj_test, nrow = bst_model[["params"]][["num_class"]], 
                             ncol = ncol(seuobj), byrow = FALSE, 
                             dimnames = list(as.character(0:(bst_model[["params"]][["num_class"]]-1)),
                                             colnames(seuobj)))

  # predict cell types
  if(celltype_assign = 1){
错误: 意外的'=' in:
"  # predict cell types
  if(celltype_assign ="

function instance

train

function instance

predict

Idents(ds0) <- ds0$seurat_clusters
ds0 <- XGBoost_predict_from_seuobj(ds0, bst_model = bst_model) %>% 
  project2ref_celltype(ref_seuobj = ds2, ref_labels = c("seurat_clusters","Classification1"))

ds1 <- XGBoost_predict_from_seuobj(ds1, bst_model = bst_model) %>% 
  project2ref_celltype(ref_seuobj = ds2, ref_labels = c("seurat_clusters","Classification1"))
umapplot(ds0, group.by = "ref_celltype")

scmap wrapper

library(SingleCellExperiment)
library(scmap)

mkref_scmap_from_seuobj <- function(ref_seuobj){
  # return a ref sce object
  ref_sce <- as.SingleCellExperiment(ref_seuobj)
  logcounts(ref_sce) <- log2(counts(ref_sce) + 1)
  
  counts(ref_sce) <- as.matrix(counts(ref_sce))
  logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))
  
  rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
  ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
  ref_sce <- selectFeatures(ref_sce, suppress_plot = T) %>% indexCell()

  return(ref_sce)
}

query_scmap_from_refsce <- function(query_seuobj, ref_sce, ref_labels = 'Classification1'){
  #return a updated seurat object with new metadata named scmap_idents
  
  query_sce <- as.SingleCellExperiment(query_seuobj)
  logcounts(query_sce) <- log2(counts(query_sce) + 1)
  
  counts(query_sce) <- as.matrix(counts(query_sce))
  logcounts(query_sce) <- as.matrix(logcounts(query_sce))
  
  rowData(query_sce)$feature_symbol <- rownames(query_sce)
  query_sce <- query_sce[!duplicated(rownames(query_sce)), ]
  query_sce <- selectFeatures(query_sce, suppress_plot = T) %>% indexCell()
  
  scmapCell_results <- scmapCell(query_sce, list(ref = metadata(ref_sce)$scmap_cell_index))
  scmapCell_clusters <- scmapCell2Cluster(scmapCell_results,
                                          list(as.character(colData(ref_sce)[[ref_labels]])))
  
  query_seuobj$scmap_idents <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ref"],
                                        row.names = colnames(query_seuobj))
  return(query_seuobj)
}
ds1 <- query_scmap_from_refsce(ds1, ref_sce)
Warning: 'isSpike' is deprecated.
See help("Deprecated")
Parameter M was not provided, will use M = n_features / 10 (if n_features <= 1000), where n_features is the number of selected features, and M = 100 otherwise.
Parameter k was not provided, will use k = sqrt(number_of_cells)
Warning: stack imbalance in '<-', 2 then 1

supervised vs unsupervised clustering

upset plot

SMC2

# library(UpSetR)

df <- cbind(ds1[["Classification1"]],ds1[["ref_celltype"]],ds1[["scmap_idents"]])
df <- df[df$Classification1 == "SMC2" | df$ref_celltype == "SMC2" | df$scmap_idents == "SMC2" ,]
li <- table(df) %>% as.data.frame() #获得含有SMC2的frequency
li <- li[!(li$Freq < 5),] #删除frequency<5的行
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))

dd$index <- factor(dd$index,levels = 1:length(levels(dd$index)))
dd$Freq <- NULL
dd <- reshape2::melt(dd,id.var = "index")
colnames(dd) <- c("index","type","name")

dt <- data.frame(Freq = li$Freq,index = as.character(1:nrow(li)))
dt$index <- factor(dt$index,levels = 1:length(levels(dt$index)))
dt$col <- dt$Freq>(sum(dt$Freq)/10) #set the color to red for those frequency > 0.1

p1 <- ggplot(dd)+geom_point(mapping = aes(x = index, y = type, color = name), size = 6) + mytheme2 + scale_color_manual(values = colors_list) + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank())
p2 <- ggplot(dt,aes(x = index, y = Freq, fill = col))+geom_bar(stat = "identity") + mytheme2 + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank(), legend.position = 'none') + scale_fill_manual(values = c("black","red"))
 
plot <- cowplot::plot_grid(p2,p1,ncol = 1,align = 'v',rel_heights = c(2,1))
plot
# ggsave("SMC2cells.svg",plot=plot,device = svg,height = 8,width = 8)

SMC1

df <- cbind(ds1[["Classification1"]],ds1[["ref_celltype"]],ds1[["scmap_idents"]])
df <- df[df$Classification1 == "SMC1" | df$ref_celltype == "SMC1" | df$scmap_idents == "SMC1" ,]
li <- table(df) %>% as.data.frame() #获得含有SMC1的frequency
li <- li[!(li$Freq < 5),] #删除frequency<5的行
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))

dd$index <- factor(dd$index,levels = 1:length(levels(dd$index)))
dd$Freq <- NULL
dd <- reshape2::melt(dd,id.var = "index")
colnames(dd) <- c("index","type","name")

dt <- data.frame(Freq = li$Freq,index = as.character(1:nrow(li)))
dt$index <- factor(dt$index,levels = 1:length(levels(dt$index)))
dt$col <- dt$Freq>(sum(dt$Freq)/10) #set the color to red for those frequency > 0.1

p1 <- ggplot(dd)+geom_point(mapping = aes(x = index, y = type, color = name), size = 6) + mytheme2 + scale_color_manual(values = colors_list) + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank())
p2 <- ggplot(dt,aes(x = index, y = Freq, fill = col))+geom_bar(stat = "identity") + mytheme2 + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank(), legend.position = 'none') + scale_fill_manual(values = c("black","red"))
 
plot <- cowplot::plot_grid(p2,p1,ncol = 1,align = 'v',rel_heights = c(2,1))
plot
ggsave("SMC1cells.svg",plot=plot,device = svg,height = 8,width = 8)

FBM

df <- cbind(ds1[["Classification1"]],ds1[["ref_celltype"]],ds1[["scmap_idents"]])
df <- df[df$Classification1 == "Fibromyocyte" | df$ref_celltype == "Fibromyocyte" | df$scmap_idents == "Fibromyocyte" ,]
li <- table(df) %>% as.data.frame() #获得含有Fibromyocyte的frequency
li <- li[!(li$Freq < 5),] #删除frequency<5的行
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))

dd$index <- factor(dd$index,levels = 1:length(levels(dd$index)))
dd$Freq <- NULL
dd <- reshape2::melt(dd,id.var = "index")
colnames(dd) <- c("index","type","name")

dt <- data.frame(Freq = li$Freq,index = as.character(1:nrow(li)))
dt$index <- factor(dt$index,levels = 1:length(levels(dt$index)))
dt$col <- dt$Freq>(sum(dt$Freq)/10) #set the color to red for those frequency > 0.1

p1 <- ggplot(dd)+geom_point(mapping = aes(x = index, y = type, color = name), size = 6) + mytheme2 + scale_color_manual(values = colors_list) + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank())
p2 <- ggplot(dt,aes(x = index, y = Freq, fill = col))+geom_bar(stat = "identity") + mytheme2 + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank(), legend.position = 'none') + scale_fill_manual(values = c("black","red"))
 
plot <- cowplot::plot_grid(p2,p1,ncol = 1,align = 'v',rel_heights = c(2,1))
plot
ggsave("Fibromyocytecells.svg",plot=plot,device = svg,height = 8,width = 8)

correlation plot

PCA

unsupervised version

ds1 <- XGBoost_predict_from_seuobj(ds1,bst_model)
Warning in XGBoost_predict_from_seuobj(ds1, bst_model) :
  Please ensure that seurat idents are in numeric forms
[1] "ARI = 0.306022542455434"
[1] "return a seurat object with meta.data'X1'~'Xn'"
[1] "return a seurat object with meta.data'projected_idents'"
ds1 <- XGBoost_predict_from_seuobj(ds1,bst_model)
Warning in XGBoost_predict_from_seuobj(ds1, bst_model) :
  Please ensure that seurat idents are in numeric forms
[1] "ARI = 0.306022542455434"
[1] "return a seurat object with meta.data'X1'~'Xn'"
[1] "return a seurat object with meta.data'projected_idents'"
ds2 <- XGBoost_predict_from_seuobj(ds2,bst_model)
Warning in XGBoost_predict_from_seuobj(ds2, bst_model) :
  强制改变过程中产生了NA
Warning in XGBoost_predict_from_seuobj(ds2, bst_model) :
  Please ensure that seurat idents are in numeric forms
[1] "ARI = 0.744493360267919"
[1] "return a seurat object with meta.data'X1'~'Xn'"
[1] "return a seurat object with meta.data'projected_idents'"
umapplot(ds0,"projected_idents") /
umapplot(ds1,"projected_idents") /
umapplot(ds2,"projected_idents")
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.
Scale for 'colour' is already present. Adding another scale for 'colour', which will replace the
existing scale.

# mergeds <- mergeds %>% SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
#     RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
#     RunUMAP(dims = 1:20) %>% 
#     FindClusters(resolution = 0.1)
# 
# umapplot(mergeds,group.by = "Classification1" ,split.by = "orig.ident")

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---
title: "XGBoost wrapper"
output: html_notebook
---

# XGBoost wrapper for seurat project in R

```{r}
source("tianfengRwrappers.R")
library(xgboost)
library(Matrix)
library(mclust)
library(tidyverse)
library(SHAPforxgboost)
library(lambda.r)

ds0 <- readRDS("ds0.rds")
ds1 <- readRDS("ds1.rds")
ds2 <- readRDS("ds2.rds")
```


```{r}
XGBoost_train_from_seuobj <- function(seuobj, is_highvar = T, test_ratio = 0.3, seed = 7)
{ 
  ## set test_ratio to 0 to avoid extracting test from dataset
  set.seed(seed)
  seuobj_label <- as.numeric(as.character(Idents(seuobj)))
  if(is.na(seuobj_label[1])) # check vaild Idents
  {
    stop("Please ensure that seurat idents are in numeric forms")
  }
  # colnames(seuobj_data) <- NULL
  seuobj_data <- get_data_table(seuobj, highvar = T, type = "data")
  xgb_param <- list(eta = 0.2, max_depth = 6, 
                    subsample = 0.6,  num_class = length(table(Idents(seuobj))),
                    objective = "multi:softprob", eval_metric = 'mlogloss')
  
  if(test_ratio == 0) {
    seuobj_train_data <- list(data = t(as(seuobj_data,"dgCMatrix")), label = seuobj_label) 
    # use whole dataset as train data
    seuobj_train <- xgb.DMatrix(data = seuobj_train_data$data,label = seuobj_train_data$label)
    bst_model <- xgb.train(xgb_param, seuobj_train, nrounds = 100, verbose = 0)
  } else {
    index <- c(1:dim(seuobj_data)[2]) %>% sample(ceiling(test_ratio*dim(seuobj_data)[2]), replace = F, prob = NULL)
    seuobj_train_data <- list(data = t(as(seuobj_data[,-index],"dgCMatrix")), label = seuobj_label[-index])
    seuobj_test_data <- list(data = t(as(seuobj_data[,index],"dgCMatrix")), label = seuobj_label[index])
    seuobj_test <- xgb.DMatrix(data = seuobj_test_data$data,label = seuobj_test_data$label)
    seuobj_train <- xgb.DMatrix(data = seuobj_train_data$data,label = seuobj_train_data$label)
    watchlist <- list(train = seuobj_train, eval = seuobj_test)
    bst_model <- xgb.train(xgb_param, seuobj_train, nrounds = 100, watchlist, verbose = 0)
  }
  return(bst_model)
}
# saveRDS(bst_model, "ds2_model.rds")

show_train_loss <- function(bst_model, nrounds = 100) #when $ test_ratio \neq 0 $ show loss in watchlist
{
  eval_loss <- bst_model[["evaluation_log"]][["eval_mlogloss"]]
  plot_ly(data.frame(eval_loss), x = c(1:nrounds), y = eval_loss) %>% 
    add_trace(type = "scatter", mode = "markers+lines", 
              marker = list(color = "black", line = list(color = "#1E90FFC7", width = 1)),
              line = list(color = "#1E90FF80", width = 2)) %>% 
    layout(xaxis = list(title = "epoch"),yaxis = list(title = "eval_mlogloss"), 
           title = "train_loss", font = list(family = "Arial", size = 25, color = "black"))
}

XGBoost_predict_from_seuobj <- function(seuobj, bst_model, is_highvar = T, seed = 7, celltype_assign = 2)
{
  #return a updated seurat object with new metadata named confidence and projected_idents
  seuobj_label <- as.numeric(as.character(Idents(seuobj)))
  if(!is.null(which(is.na(seuobj_label)))) # check vaild Idents
  {
    warning("Please ensure that seurat idents are in numeric forms") #这里似乎不跳出也可以
    seuobj_label <- as.numeric(as.character(seuobj$seurat_clusters))
  }
  temp <- get_data_table(seuobj, highvar = T, type = "data")
  seuobj_data <- matrix(data = 0, nrow = bst_model$nfeatures, ncol = length(colnames(temp)), 
                     byrow = FALSE, dimnames = list(bst_model[["feature_names"]],colnames(temp)))
  intersect_features <- intersect(bst_model[["feature_names"]], rownames(temp))
  seuobj_data[intersect_features,] <- temp[intersect_features,]
  rm(temp)
  

  # colnames(seuobj_data) <- NULL
  seuobj_test_data <- list(data = t(as(seuobj_data,"dgCMatrix")), label = seuobj_label)
  seuobj_test <- xgb.DMatrix(data = seuobj_test_data$data,label = seuobj_test_data$label)
  
  #预测结果
  predict_seuobj_test <- predict(bst_model, newdata = seuobj_test)
  
  predict_prop_seuobj <<- matrix(data=predict_seuobj_test, nrow = bst_model[["params"]][["num_class"]], 
                             ncol = ncol(seuobj), byrow = FALSE, 
                             dimnames = list(as.character(0:(bst_model[["params"]][["num_class"]]-1)),
                                             colnames(seuobj)))

  # predict cell types
  if(celltype_assign == 1){
    seuobj_res <- apply(predict_prop_seuobj,2,ident_assignfunc,rownames(predict_prop_seuobj))
  }else if(celltype_assign == 2){
    seuobj_res <- apply(predict_prop_seuobj,2,ident_assignfunc2,rownames(predict_prop_seuobj))
  }
  
  print(paste('ARI =',adjustedRandIndex(seuobj_res, seuobj_test_data$label)))
  
  seuobj <- AddMetaData(seuobj, data.frame(t(predict_prop_seuobj), stringsAsFactors=F))
  print("return a seurat object with meta.data'X1'~'Xn'")
  
  #save and update seurat object
  seuobj$projected_idents <- factor(seuobj_res, levels =
                                      c(as.character(0:(bst_model[["params"]][["num_class"]]-1)),"unassigned"))
  print("return a seurat object with meta.data'projected_idents'")
  return(seuobj)
}

## assign cell type predicted via tree models, consider confidence
ident_assignfunc <- function(s, ident) {
    if (max(s) > 1.5 / length(ident)) {
          return(ident[which(s == max(s))])
      } else {
          return("unassigned")
      }
}

ident_assignfunc2 <- function(s, ident)
# confidence : max - 2th_max > 0.4
{
    if (max(s) - max(s[s!=max(s)]) > 0.4) {
          return(ident[which(s == max(s))])
      } else {
          return("unassigned")
      }
}

project2ref_celltype <- function(query_seuobj, ref_seuobj, 
                                 query_labels = "projected_idents",
                                 ref_labels = c("seurat_clusters","Classification1")) 
  # transfer lables: add ref_celltype to meta.data in query seurat object
{
  identmap <- levels(ref_seuobj@meta.data[[ref_labels[1]]]) 
  ## build mapping between numeric labels and ref labels
  names(identmap) <- levels(ref_seuobj@meta.data[[ref_labels[2]]]) 
  
  df <- query_seuobj@meta.data[[query_labels]]
  lambda(x,identmap) %:=% ifelse(x=="unassigned",x,names(identmap[identmap == x]))
  levels(df) <- lapply(levels(df), lambda, identmap) %>% as.character() # permute cell labels via `identmap`
  query_seuobj$ref_celltype <- df
  return(query_seuobj)
}

```

---
## function instance
### train
```{r}
umapplot(ds2, group.by = "seurat_clusters")
Idents(ds2) <- ds2$seurat_clusters
bst_model <- XGBoost_train_from_seuobj(ds2)
show_train_loss(bst_model)
```

## function instance
### predict
```{r}
Idents(ds0) <- ds0$seurat_clusters
ds0 <- XGBoost_predict_from_seuobj(ds0, bst_model = bst_model) %>% 
  project2ref_celltype(ref_seuobj = ds2, ref_labels = c("seurat_clusters","Classification1"))

ds1 <- XGBoost_predict_from_seuobj(ds1, bst_model = bst_model) %>% 
  project2ref_celltype(ref_seuobj = ds2, ref_labels = c("seurat_clusters","Classification1"))
umapplot(ds0, group.by = "ref_celltype")

```
## scmap wrapper
```{r}
library(SingleCellExperiment)
library(scmap)

mkref_scmap_from_seuobj <- function(ref_seuobj){
  # return a ref sce object
  ref_sce <- as.SingleCellExperiment(ref_seuobj)
  logcounts(ref_sce) <- log2(counts(ref_sce) + 1)
  
  counts(ref_sce) <- as.matrix(counts(ref_sce))
  logcounts(ref_sce) <- as.matrix(logcounts(ref_sce))
  
  rowData(ref_sce)$feature_symbol <- rownames(ref_sce)
  ref_sce <- ref_sce[!duplicated(rownames(ref_sce)), ]
  ref_sce <- selectFeatures(ref_sce, suppress_plot = T) %>% indexCell()

  return(ref_sce)
}

query_scmap_from_refsce <- function(query_seuobj, ref_sce, ref_labels = 'Classification1'){
  #return a updated seurat object with new metadata named scmap_idents
  
  query_sce <- as.SingleCellExperiment(query_seuobj)
  logcounts(query_sce) <- log2(counts(query_sce) + 1)
  
  counts(query_sce) <- as.matrix(counts(query_sce))
  logcounts(query_sce) <- as.matrix(logcounts(query_sce))
  
  rowData(query_sce)$feature_symbol <- rownames(query_sce)
  query_sce <- query_sce[!duplicated(rownames(query_sce)), ]
  query_sce <- selectFeatures(query_sce, suppress_plot = T) %>% indexCell()
  
  scmapCell_results <- scmapCell(query_sce, list(ref = metadata(ref_sce)$scmap_cell_index))
  scmapCell_clusters <- scmapCell2Cluster(scmapCell_results,
                                          list(as.character(colData(ref_sce)[[ref_labels]])))
  
  query_seuobj$scmap_idents <- data.frame(scmapCell_clusters$scmap_cluster_labs[,"ref"],
                                        row.names = colnames(query_seuobj))
  return(query_seuobj)
}
```

```{r}
ref_sce <- mkref_scmap_from_seuobj(ds2)
ds0 <- query_scmap_from_refsce(ds0, ref_sce)
ds1 <- query_scmap_from_refsce(ds1, ref_sce)
umapplot(ds0, group.by = "scmap_idents")
umapplot(ds0, group.by = "ref_celltype")
table(ds0$ref_celltype)
table(ds0$scmap_idents)
adjustedRandIndex(ds0$ref_celltype, ds0$seurat_clusters)
adjustedRandIndex(ds0$scmap_idents, ds0$seurat_clusters)

```



## supervised vs unsupervised clustering
### upset plot
#### SMC2
```{r}
# library(UpSetR)

df <- cbind(ds1[["Classification1"]],ds1[["ref_celltype"]],ds1[["scmap_idents"]])
df <- df[df$Classification1 == "SMC2" | df$ref_celltype == "SMC2" | df$scmap_idents == "SMC2" ,]
li <- table(df) %>% as.data.frame() #获得含有SMC2的frequency
li <- li[!(li$Freq < 5),] #删除frequency<5的行
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))

dd$index <- factor(dd$index,levels = 1:length(levels(dd$index)))
dd$Freq <- NULL
dd <- reshape2::melt(dd,id.var = "index")
colnames(dd) <- c("index","type","name")

dt <- data.frame(Freq = li$Freq,index = as.character(1:nrow(li)))
dt$index <- factor(dt$index,levels = 1:length(levels(dt$index)))
dt$col <- dt$Freq>(sum(dt$Freq)/10) #set the color to red for those frequency > 0.1

p1 <- ggplot(dd)+geom_point(mapping = aes(x = index, y = type, color = name), size = 6) + mytheme2 + scale_color_manual(values = colors_list) + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank())
p2 <- ggplot(dt,aes(x = index, y = Freq, fill = col))+geom_bar(stat = "identity") + mytheme2 + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank(), legend.position = 'none') + scale_fill_manual(values = c("black","red"))
 
plot <- cowplot::plot_grid(p2,p1,ncol = 1,align = 'v',rel_heights = c(2,1))
plot
# ggsave("SMC2cells.svg",plot=plot,device = svg,height = 8,width = 8)
```

#### SMC1
```{r}
df <- cbind(ds1[["Classification1"]],ds1[["ref_celltype"]],ds1[["scmap_idents"]])
df <- df[df$Classification1 == "SMC1" | df$ref_celltype == "SMC1" | df$scmap_idents == "SMC1" ,]
li <- table(df) %>% as.data.frame() #获得含有SMC1的frequency
li <- li[!(li$Freq < 5),] #删除frequency<5的行
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))

dd$index <- factor(dd$index,levels = 1:length(levels(dd$index)))
dd$Freq <- NULL
dd <- reshape2::melt(dd,id.var = "index")
colnames(dd) <- c("index","type","name")

dt <- data.frame(Freq = li$Freq,index = as.character(1:nrow(li)))
dt$index <- factor(dt$index,levels = 1:length(levels(dt$index)))
dt$col <- dt$Freq>(sum(dt$Freq)/10) #set the color to red for those frequency > 0.1

p1 <- ggplot(dd)+geom_point(mapping = aes(x = index, y = type, color = name), size = 6) + mytheme2 + scale_color_manual(values = colors_list) + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank())
p2 <- ggplot(dt,aes(x = index, y = Freq, fill = col))+geom_bar(stat = "identity") + mytheme2 + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank(), legend.position = 'none') + scale_fill_manual(values = c("black","red"))
 
plot <- cowplot::plot_grid(p2,p1,ncol = 1,align = 'v',rel_heights = c(2,1))
plot
ggsave("SMC1cells.svg",plot=plot,device = svg,height = 8,width = 8)
```

#### FBM
```{r}
df <- cbind(ds1[["Classification1"]],ds1[["ref_celltype"]],ds1[["scmap_idents"]])
df <- df[df$Classification1 == "Fibromyocyte" | df$ref_celltype == "Fibromyocyte" | df$scmap_idents == "Fibromyocyte" ,]
li <- table(df) %>% as.data.frame() #获得含有Fibromyocyte的frequency
li <- li[!(li$Freq < 5),] #删除frequency<5的行
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))

dd$index <- factor(dd$index,levels = 1:length(levels(dd$index)))
dd$Freq <- NULL
dd <- reshape2::melt(dd,id.var = "index")
colnames(dd) <- c("index","type","name")

dt <- data.frame(Freq = li$Freq,index = as.character(1:nrow(li)))
dt$index <- factor(dt$index,levels = 1:length(levels(dt$index)))
dt$col <- dt$Freq>(sum(dt$Freq)/10) #set the color to red for those frequency > 0.1

p1 <- ggplot(dd)+geom_point(mapping = aes(x = index, y = type, color = name), size = 6) + mytheme2 + scale_color_manual(values = colors_list) + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank())
p2 <- ggplot(dt,aes(x = index, y = Freq, fill = col))+geom_bar(stat = "identity") + mytheme2 + theme(axis.ticks.x = element_blank(),axis.title.x = element_blank(), legend.position = 'none') + scale_fill_manual(values = c("black","red"))
 
plot <- cowplot::plot_grid(p2,p1,ncol = 1,align = 'v',rel_heights = c(2,1))
plot
ggsave("Fibromyocytecells.svg",plot=plot,device = svg,height = 8,width = 8)
```

## correlation plot
```{r}
scmap <- ds1$scmap_idents
xgb <- ds1$ref_celltype
ref <- ds2$Classification1

geneset1 <- read.table("SMC")
ds1 <- AddModuleScore(ds1,features = geneset1, name = 'SMC_score')
ds2 <- AddModuleScore(ds2,features = geneset1, name = 'SMC_score')
geneset2 <- read.table("FB")
ds1 <- AddModuleScore(ds1,features = geneset2, name = 'FB_score')
ds2 <- AddModuleScore(ds2,features = geneset2, name = 'FB_score')
# f("SMC_score1",label = F, ds1) + scale_colour_gradient(low="#1E90FF", high="#ff2121")
# f("SMC_score1",label = F, ds2) + scale_colour_gradient(low="#1E90FF", high="#ff2121")

df1 <- data.frame(FetchData(ds1, c("SMC_score1","FB_score1"), cells = names(scmap[scmap == "Fibromyocyte"])),type = "scmap") 
df2 <- data.frame(FetchData(ds1,c("SMC_score1","FB_score1"), cells = names(xgb[xgb == "Fibromyocyte"])),type = "xgbtree") 
df3 <- data.frame(FetchData(ds2,c("SMC_score1","FB_score1"), cells = names(ref[ref == "Fibromyocyte"])),type = "reference") 

df <- rbind(df1,df2,df3)

ggboxplot(df, x = "type", y = "SMC_score1", color = "type", add = "jitter") +
  stat_compare_means(comparisons =
                       list(c("scmap","reference"),c("xgbtree","reference")), 
                     method = "wilcox.test") 

ggboxplot(df, x = "type", y = "FB_score1", color = "type", add = "jitter") +
  stat_compare_means(comparisons =
                       list(c("scmap","reference"),c("xgbtree","reference")), 
                     method = "wilcox.test") 

df <- data.frame(row.names = selected_features)
for(cell_type in c("SMC1","Fibromyocyte","SMC2")){
  df1 <- data.frame(FetchData(ds1, selected_features, cells = names(scmap[scmap == cell_type]))) %>% colMeans() 
  df2 <- data.frame(FetchData(ds1, selected_features, cells = names(xgb[xgb == cell_type]))) %>% colMeans()
  df3 <- data.frame(FetchData(ds2, selected_features, cells = names(ref[ref == cell_type]))) %>% colMeans()
  df[paste0("ref_",cell_type)] <- df3
  df[paste0("xgb_",cell_type)] <- df2
  df[paste0("scmap_",cell_type)] <- df1
}

corr <- cor(df)
pheatmap::pheatmap(corr,breaks = unique(c(seq(0.6, 1, length = 100))),
        color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(100),
        border_color = NA, cluster_rows = T, cluster_cols = T,
        main = "corr", angle_col = 45, show_rownames = T)

library(ggcor)
quickcor(df, type = "upper") + geom_circle2()
```
### PCA
```{r}
selected_features <-intersect(FindVariableFeatures(ds1, nfeatures = 200)@assays[["SCT"]]@var.features,
                              FindVariableFeatures(ds2, nfeatures = 200)@assays[["SCT"]]@var.features)

selected_features <- read.csv("./datatable/ds2_features.csv")
selected_features <- as.character(selected_features$Feature[1:50])
selected_features <- intersect(selected_features, VariableFeatures(ds1))
res <- data.frame(row.names = c("A","B","C"))

for(cell_type in c("SMC1","Fibromyocyte","SMC2")){
  df1 <- data.frame(FetchData(ds1, selected_features, cells = names(scmap[scmap == cell_type])),type = "scmap") 
  df2 <- data.frame(FetchData(ds1,selected_features, cells = names(xgb[xgb == cell_type])),type = "xgbtree") 
  df3 <- data.frame(FetchData(ds2,selected_features, cells = names(ref[ref == cell_type])),type = "reference") 
  
  df <- rbind(df1,df2,df3)
  
  PCAres <- FactoMineR::PCA(df[,c(-ncol(df))],ncp = 5, graph = F)
  
  dd <- cbind(PCAres[["ind"]][["coord"]],data.frame(type = df[,c(ncol(df))]))
  
  # ggplot(dd)+geom_point(aes(x = Dim.1, y = Dim.2, color = type))
  v_scmap <- dd[dd$type == "scmap",c(1:5)] %>% colMeans() %>% as.matrix()
  v_xgb <- dd[dd$type == "xgbtree",c(1:5)] %>% colMeans() %>% as.matrix()
  v_ref <- dd[dd$type == "reference",c(1:5)] %>% colMeans() %>% as.matrix()
  v <- c(norm(v_scmap-v_ref),norm(v_xgb-v_ref),norm(v_xgb-v_scmap))
  res[cell_type] <- v
}
res
```


## unsupervised version
```{r}
umapplot(ds1)
umapplot(ds2)
umapplot(ds0)

bst_model <- XGBoost_train_from_seuobj(ds0)
ds2 <- XGBoost_predict_from_seuobj(ds2,bst_model,celltype_assign = 1)
# Idents(ds2) <- ds2$seurat_clusters
bst_model <- XGBoost_train_from_seuobj(ds2)
ds0 <- XGBoost_predict_from_seuobj(ds0,bst_model,celltype_assign = 1)

umapplot(ds2,group.by = "projected_idents")
confuse_matrix <- table(ds2$seurat_clusters, ds2$projected_idents, dnn=c("true","pre"))

sankey_plot(confuse_matrix = confuse_matrix,
            dimnames(confuse_matrix)$pre,dimnames(confuse_matrix)$true,
            session = "ds2 is projected to ds1")

# choose train dataset (merged)

df <- cbind(ds2[["seurat_clusters"]],ds2[["projected_idents"]])
li <- table(df) %>% as.data.frame() 
li <- li[!(li$Freq < 50),] #删除frequency<50的细胞类型组合
li <- li[order(li$Freq,decreasing = T),]
dd <- data.frame(li,index = as.character(1:nrow(li)))


confuse_matrix[confuse_matrix<50] <- 0 #删除frequency<50的细胞类型
confuse_matrix


# find embedding pattern
temp <- prop.table(confuse_matrix,margin = 1) #unsupervised labels关于supervised labels的组成
temp2 <- prop.table(confuse_matrix,margin = 2) #supervised labels关于unsupervised labels的组成

temp[is.na(temp)] <- 0
temp2[is.na(temp2)] <- 0

tempm <- reshape2::melt(temp)

td <- c()
for(i in which(temp>0.7)){
  if(temp2[i] < 0.7){
    td <- append(td,i)
  }
}
tempm <- tempm[td,] #保存点对(unsupervised clusters, supervised clusters)
tempm$value <- NULL

##修改用来构造初始树模型的数据集(ds0)分群
ref_idents <- ds0$seurat_clusters #最开始用来构造树的分类

for(celltype in tempm$true){
  n <- names(ds0$projected_idents[ds0$projected_idents == celltype]) ##需要改变为embedding的细胞
  ref_idents <- factor(ref_idents, levels = c(levels(ref_idents),10+celltype))
  ref_idents[n] <- 10+celltype
  
}
Idents(ds0) <- ref_idents


##用来修正树的数据集(ds2)分群
mod_idents <- ds2$projected_idents
for(celltype in tempm$true){
  n <- names(ds2$seurat_clusters[ds2$seurat_clusters == celltype]) ##需要改变为embedding的细胞
  mod_idents <- factor(mod_idents, levels = c(levels(mod_idents),10+celltype))
  mod_idents[n] <- 10+celltype
}
### ds2的独特细胞类型，判断embedding模式

df <- table(ds0$projected_idents) %>% prop.table()
unique_celltypes <- as.numeric(names(df[df<0.01])) #投射之后少于1%，使用无监督分群
# table(ds0$projected_idents)
for(celltype in unique_celltypes){
  n <- names(ds2$seurat_clusters[ds2$seurat_clusters == celltype]) ##需要改变为embedding的细胞
  mod_idents <- factor(mod_idents, levels = c(levels(mod_idents),20+celltype))
  mod_idents[n] <- 20+celltype
}


# umapplot(ds2,"projected_idents")
Idents(ds2) <- mod_idents

mergeds <- merge(ds0,subset(ds2,idents = "unassigned",invert = T))
levels(Idents(mergeds)) <- c(0:(length(levels(Idents(mergeds)))-1)) ##重命名因子水平
seuobj_label <- as.numeric(as.character(Idents(mergeds))) #原始训练数据

temp <- as.matrix(GetAssayData(mergeds, slot = "data"))
genelist <- GetAssayData(ds0, slot = "var.features")
temp <- temp[genelist, ] # 过滤高变异基因

seuobj_data <- matrix(data = 0, nrow = bst_model$nfeatures, ncol = length(colnames(temp)), 
                   byrow = FALSE, dimnames = list(bst_model[["feature_names"]],colnames(temp)))
intersect_features <- intersect(bst_model[["feature_names"]], rownames(temp))
seuobj_data[intersect_features,] <- temp[intersect_features,]
rm(temp)

## 额外增加的细胞信息

xgb_param <- list(eta = 0.2, max_depth = 6, 
                subsample = 0.6,  num_class = length(table(Idents(mergeds))),
                objective = "multi:softprob", eval_metric = 'mlogloss')


seuobj_train_data <- list(data = t(as(seuobj_data,"dgCMatrix")), label = seuobj_label) 
# use whole dataset as train data
seuobj_train <- xgb.DMatrix(data = seuobj_train_data$data,label = seuobj_train_data$label)
bst_model <- xgb.train(xgb_param, seuobj_train, nrounds = 50, verbose = 0)


ds0 <- XGBoost_predict_from_seuobj(ds0,bst_model)
ds1 <- XGBoost_predict_from_seuobj(ds1,bst_model)
ds2 <- XGBoost_predict_from_seuobj(ds2,bst_model)
```


```{r fig.width=4, fig.height=8}
umapplot(ds0,"projected_idents") /
umapplot(ds1,"projected_idents") /
umapplot(ds2,"projected_idents")


levels(ds0$projected_idents)
```

```{r}
# mergeds <- mergeds %>% SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
#     RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
#     RunUMAP(dims = 1:20) %>% 
#     FindClusters(resolution = 0.1)
# 
# umapplot(mergeds,group.by = "Classification1" ,split.by = "orig.ident")
```


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